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Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability. (arXiv:2201.08671v1 [cs.LG])
Jan. 24, 2022, 2:10 a.m. | Alireza Koochali, Peter Schichtel, Andreas Dengel, Sheraz Ahmed
cs.LG updates on arXiv.org arxiv.org
The recent developments in the machine learning domain have enabled the
development of complex multivariate probabilistic forecasting models.
Therefore, it is pivotal to have a precise evaluation method to gauge the
performance and predictability power of these complex methods. To do so,
several evaluation metrics have been proposed in the past (such as Energy
Score, Dawid-Sebastiani score, variogram score), however, they cannot reliably
measure the performance of a probabilistic forecaster. Recently, CRPS-sum has
gained a lot of prominence as a …
More from arxiv.org / cs.LG updates on arXiv.org
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